Awaken the Machines! Investing in Artificial Intelligence and Machine Learning

Delwin Graham - May 10, 2019
Machine learning and artificial intelligence is happening now. How can we profit?

Artificial intelligence and machine learning are new categories of computer science which transition computers from mere followers of instructions to machines capable of “thought”. Of course, we have long considered and feared the possibility of machines thinking. In 1950, Allan Turing developed a test of a machine’s ability to exhibit intelligent behaviour equivalent or indistinguishable from that of a human. In the present case, machines are considered to be “intelligent” to the extent that they are able to improve performance, that is, to “learn” without human intervention.

Machine learning is arguably one of the most important General Purpose Technologies (GPTs) of our generation (Cf. Daud Khan and Paul Morland, Software and Services: Industry Update; Canaccord Genuity Limited (UK) November 28, 2017). GPTs are technological innovations which have had a wide-ranging impact on multiple industries. Examples include the wheel, electricity, the steam engine, the Internet and the internal combustion engine. They all have reshaped the economy and ultimately boosted productivity across all sectors and industries. However, the ability of machines to continually improve with or without human intervention turns much of this technology landscape on its head. Technology has been driven by developers who codify actions which the machine then repeats faster and more accurately than a human could. However, machine learning tackles problems that are difficult to codify because we, as humans, struggle to understand how we get to a particular answer. For example, how do we recognize faces?

With the proliferation and retention of more data, machines can become more adept at recognizing patterns in diverse sets of data to come up with better answers and ultimately a competitive advantage for users. Here are a few illustrations: In the case of cyber security, most anti-virus systems use signature-based techniques to block threats, but these are becoming redundant with new attack sites (e.g., Internet of Things (IoT) devices, mobile, etc.) and methods using artificial intelligence. With its ability to learn quickly with a large set of ongoing test data, machine learning can make immediate decisions to enable a rapid response to prevent threats rather than simply reacting after the fact. One significant advantage over signature-based techniques is the ability to detect minor differences in the executed code.

In the case of financial fraud detection, financial institutions typically rely on historic data to create a pattern of potential fraudulent transactions. Similar to legacy anti-virus systems, this is signature-based protection that does little to detect first-time fraud. Also, the fraud models are often updated infrequently due to the cost and time required for accurate modelling, and this can lead to fraud schemes remaining undetected for long periods of time. The other concern for banks is to maximize customer satisfaction and thus minimize false positives, where legitimate transactions are flagged as fraudulent. By modelling and predicting individual behavior in real-time, machine learning can serve to detect a change in an individual’s behaviour and revaluate the risk, thus blocking new fraudulent attacks in real time and reducing false positives.

Machine learning is already being used to generate personalized customer offers. Amazon’s recommendation engine has huge success in driving incremental purchases by integrating it throughout the buying process, from product discovery, checkout and after sales. In fact, around 35% of all Amazon sales are estimated to be generated by the recommendation engine (source: Khan). Amazon also uses AI technology for its Alexa–powered devices like Echo. Netflix uses machine learning to access the content you watch and predict the content that you might enjoy. Google’s parent company, Alphabet, has made a series of acquisitions that cover the entire gamut of the AI industry, including natural language processing, speech generation and image analysis, and has built its own servers to integrate AI software into its products and services, including the Google Assistant home speaker.

Machine learning and artificial intelligence is happening now. How can we profit? Researchers at the cutting edge of AI are working on general AI, which aims to develop intelligent computers that can think and plan across broad areas, teaching themselves just like humans (Cf., Chris Menon, How to Invest in Artificial Intelligence, November 21, 2018). But the most promising near-term uses would seem to be in ‘narrow AI systems’, like Amazon’s recommendation engine and Google’s home speaker assistant. Artificial‑intelligence systems are becoming entrenched in the operating background of corporations, providing further efficiencies and greater profits. While certain companies work to enable these AI systems, like microchip manufacturers Nvidia and Xilinx, the companies that control the data are most likely to benefit from machine learning in the near‑term. To this point, the FAANGs (Facebook, Apple, Amazon, Netflix, Google) and Microsoft have been active acquirers of smart algorithms.

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